Objective This study evaluates pre-surgery white matter abnormalities in evaluating the association between pre-surgical white matter integrity and post-surgery outcomes at the group level using Diffusion Tensor Imaging (DTI) in patients with Mesial Temporal Lobe Epilepsy (MTLE), specifically examining the differences between seizure-free and non-seizure-free patients, by Regions of Interest (ROIs) and diffusion parameters (biomarkers). Methods A retrospective analysis was conducted on pre-surgical DTI data from MTLE patients who underwent amygdalohippocampectomy with the subject outcome evaluation. Key parameters, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusion (AD), and radial diffusion (RD), were extracted from regions of interest (ROIs) such as the hippocampus, thalamus, and temporal pole. Statistical analysis included ANOVA with Bonferroni post-hoc tests to compare surgical responders and non-responders on the ipsilateral and contralateral sides of the epileptogenic zone, as per clinical convention. Results FA values in the contralateral hippocampus and thalamus were significantly lower in the Not-SF group compared to the SF group ( p < 0.05). RD values in the contralateral hippocampus and temporal pole were significantly higher in the Not-SF group ( p < 0.05). In ipsilateral regions, MD and RD values in the hippocampus and temporal pole were significantly higher in the Not-SF group ( p < 0.01), while FA values were significantly lower ( p < 0.001). AD did not show significant differences. Overall, increased RD and MD and reduced FA in the Not-SF group indicate reduced microstructural integrity associated with poor seizure control. Discussion This study demonstrates that diffusion-based structural connectivity analysis can distinguish seizure-free from non–seizure-free TLE patients, highlighting the clinical relevance of hippocampus–temporal pole and hippocampus–thalamus pathways in postoperative outcomes. By extending diffusion evaluation beyond traditional tracts and integrating modern segmentation and tractography tools, our findings support the role of TLE as a network disorder and emphasize the potential of advanced DTI methods for understanding the network-level changes associated with surgical outcomes. Conclusion This study shows that diffusion-based structural connectivity metrics, particularly within hippocampus–temporal pole and hippocampus–thalamus pathways, showed exploratory associations with surgical outcomes in MTLE patients. While these metrics suggest differences between outcome groups, these findings are hypothesis-generating.
Introduction Gaussian noise is often added during the acquisition or transmission of medical images, which can blur important organs and reduce diagnostic accuracy. To address this issue, a hybrid denoising model that combines BayesShrink thresholding in the wavelet domain with Patch-Wise Local Principal Component Analysis (PLPCA) is proposed. Methods The framework initially utilises the concept of local patch redundancy by applying PLCA to effectively suppress noise while preserving finer details. The remaining noise is again decomposed using the wavelet transform, with adaptive BayesShrink thresholding applied to refine the coefficients. Subsequently, the reconstructed signal is obtained. The resultant denoised images are then combined to obtain the final enhanced image. HRCTs and MRI datasets were corrupted by Gaussian noise at σ = 10-40 and were validated. Results Quantitative analysis based on PSNR, entropy, BRISQUE, NIQE, and PIQE confirmed the consistent high impact of the proposed method across traditional, hybrid, and deep learning baselines. It is worth noting that the approach achieved PSNR values of 31.15 dB at HRCT and 33.00 dB at MRI when σ = 10, as well as at high noise levels (23.95 dB and 22.64 dB, respectively). Discussion The proposed method consistently excels over traditional, hybrid, and deep learning-based strategies, as shown by quantitative assessments using PSNR, entropy, BRISQUE, NIQE, and PIQE. Conclusion The suggested framework has the strength of suppressing Gaussian noise while retaining anatomical detail. Thus, it is an encouraging option for enhancing the quality of medical image diagnosis in clinical practice.
Introduction Magnetic Resonance Imaging (MRI) and High-Resolution Computed Tomography (HRCT) are crucial for comprehensive diagnosis and treatment planning, as they provide detailed anatomical information. However, noise introduced during image acquisition often degrades the quality of these images, obscuring key anatomical features and complicating accurate diagnoses. Methods This study compared the performance of eight denoising algorithms: BM3D, EPLL, FoE, WNNM, Bilateral, Guided, NLM, and DnCNN. Both objective metrics, including Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), as well as perceptual quality metrics, such as NIQE, BRISQUE, and PIQE, were employed to assess their effectiveness. Results BM3D consistently outperformed other algorithms at low and moderate noise levels, achieving the highest PSNR and SSIM values while preserving structural integrity and perceptual quality. For high noise levels, conventional algorithms, such as EPLL and WNNM, demonstrated competitive performance in homogeneous areas, preserving fine texture, but were limited by computational complexity. Discussion One of the challenges in image denoising is preserving the finer detail structures of images while efficiently removing noise. Finding a balance between the reduction of noise and preservation of image integrity can be a lifesaving challenge, especially in cases where the images are in high detail, such as in the medical world. Conclusion This study highlights the trade-offs between denoising quality and computational efficiency among various algorithms for MRI and HRCT images. While BM3D remains a dependable choice for moderate noise levels, advanced deep learning-based methods, such as DnCNN, are better suited for handling significant noise variations without compromising critical diagnostic features.
Introduction Differentiating brain tumors through neuroimaging is challenging due to overlapping radiological features, requiring advanced techniques and clinical correlation for accurate diagnosis. The aim of this retrospective observational monocentric study is to determine the diagnostic performance of combining perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) for MRI-based differential diagnosis of the three major classes of adult malignant intra-axial brain tumors. Principal component analysis (PCA) is applied to identify relevant imaging features, with the goal of supporting preoperative diagnosis beyond conventional MRI alone. Methods We selected 72 adult patients who underwent MRI examination, including DWI, PWI, and MRS imaging before surgery, for suspected malignant intra-axial expansive lesions (namely glioblastoma, metastasis, or primary non-Hodgkin lymphoma). The definitive histological diagnosis was obtained on post-operative specimens. Quantitative variables derived from DWI, PWI, and MRS acquisition were identified and processed using principal component analysis. The differences between groups for the most relevant parameters identified by PCA were then tested by the Kruskal-Wallis test. Results Finally, a total of 11 specimens of non-Hodgkin lymphomas, 18 specimens of single metastases, and 43 specimens of wild-type glioblastomas were gathered. CBF, CBV, MTT, ADC, and lipid-lactate (Lip-Lac) at MRS were found to be the most relevant variables for differential diagnostic purposes through PCA analysis. In particular, ADC and Lip-Lac were more strongly associated with differentiating lymphoma from the other two disease classes, while CBF, CBV, and MTT contributed more to differentiating glioblastoma from metastasis. Discussion In this study, ADC and Lip-Lac differentiated CNS lymphoma, while CBV, CBF, and MTT distinguished GBM from metastases, supporting PCA’s clinical value beyond diagnostic workflows. Conclusion The combined use of PWI, DWI, and MRS can assist the radiologist in accurate preoperative differential diagnosis of the three main classes of adult malignant intra-axial brain neoplasms, enhancing diagnostic performance beyond that of conventional MRI alone.
Introduction Ischemic stroke remains a leading cause of disability and mortality, making a rapid and reliable diagnosis essential. Dynamic Susceptibility Contrast Magnetic Resonance Imaging (DSC-MRI) is widely used to assess cerebral perfusion, yet its diagnostic accuracy strongly depends on the computational model applied. This study investigates how model selection influences the reliability of CBV-based ischemic stroke detection under varying noise conditions and tissue types. Methods Simulated tissue signal curves were generated from clinical reference data and modified to reflect ischemic alterations across multiple noise levels. Cerebral Blood Volume (CBV) was estimated using two established approaches: the modified gamma variate function and a compartmental (triple-exponential) model. Diagnostic performance was evaluated by comparing the accuracy and robustness of CBV estimation. Results The compartmental model consistently outperformed the gamma variate function, providing more accurate and stable CBV estimates, particularly under high-noise conditions. In contrast, the gamma variate function demonstrated reduced robustness and greater sensitivity to noise. Discussion These findings underscore the importance of computational model selection in DSC-MRI analysis. The performance of the compartmental model suggests its potential for integration into clinical workflows, particularly in acute stroke care, where reliability under challenging conditions is crucial. However, this study has several limitations. Most importantly, the analysis was based on simulated tissue signal curves derived from clinical reference data rather than on in vivo measurements, which may not fully capture the complexity of real patient physiology. Conclusion Computational modeling influences the diagnostic value of DSC-MRI in ischemic stroke assessment. The compartmental model offers greater robustness and accuracy, supporting its use in diagnostic systems.
Multi-modal Medical Image Fusion (MMIF) is an advancing field at the intersection of medical imaging, data science, and clinical diagnostics. It aims to integrate complementary data from various imaging modalities, such as MRI, CT, and PET, into a single, diagnostically superior composite image. The limitations of unimodal imaging, such as low spatial resolution, insufficient contrast, or incomplete functional characterization, have catalyzed the development of MMIF techniques to enable enhanced visualization, precise diagnosis, and personalized therapeutic planning. This review provides a comprehensive synthesis of the MMIF landscape, categorizing methodologies into five principal domains such as spatial, frequency-based, sparse representation, deep learning, and hybrid approaches. Each technique is critically evaluated for its advantages, limitations, and applicability in clinical settings. Preprocessing, registration, fusion execution, and validation are covered in this review, along with levels of fusion pixel, feature, and decision. The study reviews prominent public databases, including TCIA, OASIS, ADNI, MIDAS, AANLIB, and DDSM, comparing their imaging modalities, disease coverage, file formats, and accessibility. The evaluation of MMIF techniques is systematically addressed, providing a framework for objective performance assessment. An experimental setup is implemented on two datasets to assess the comparative efficacy of selected MMIF techniques utilizing quantitative evaluation variables such as SSIM, entropy, spatial frequency, and mutual information. The results highlight the effectiveness of hybrid and deep learning-based approaches in maintaining both anatomical detail and functional consistency across modalities. The review explores MMIF’s real-world clinical applications, including image-guided neurosurgery, spinal planning, stereotactic radiosurgery, orthopedic implant design, and oncology diagnostics. It also provides insights into future directions, such as explainable AI, federated learning, and integration with robotic surgeries. MMIF offers immense potential yet has limitations like registration errors, computational burdens, generation of artifacts, loss of specific information, and a lack of standardized evaluation metrics. Essentially, the study provides an analytical basis for healthcare experts, scientists, and engineers aiming to develop clinically scalable MMIF systems, which will become indispensable tools for improving diagnostic accuracy, treatment planning, and patient outcomes in modern healthcare.
Introduction The glymphatic system is a waste clearance pathway within the brain that relies on the flow of cerebrospinal fluid facilitated by astrocytes. It has been proposed that this glymphatic system can be observed using Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS). Yet, all observations have been made at 3.0T while most clinical scanners worldwide operate at 1.5T. The change in magnetic field strength is significant, as it affects signal-to-noise ratio, spatial resolution, and the minimum echo time achievable for the same diffusion weighting, which implies different diffusion times embedded in the observations. The question remains on the usefulness of this index at 1.5T for the observation of pathologies, particularly those related to glial cells. This study aimed to evaluate the usability of the DTI-ALPS index as a biomarker for the glymphatic system using 1.5T MRI, focusing on reproducibility among different users and its capacity to distinguish pathological values in glioma patients. Materials and Methods A retrospective study included 44 glioma patients and 10 healthy volunteers, with DTI sequences acquired using a 1.5T MRI scanner. Patients whose structural anatomy at the level of the lateral ventricle was significantly modified by the tumor were excluded. Reproducibility between sessions and different users was evaluated on 16 healthy subjects from a public dataset. The ALPS index was calculated based on diffusivity measurements in the projection and association fibers. Four neuroradiologists independently placed regions of interest for ALPS index calculation. Statistical analyses included Intraclass Correlation Coefficients (ICC) to assess inter-rater reliability and linear regression models to analyze the relationship between ALPS index values and patient characteristics. Results In the data from healthy subjects, the inter-rater reliability was low (ICC = 0.34), indicating high variability among users. A negative correlation between the ALPS index and age was observed. In glioma patients, the ALPS index showed significant differences between ipsilateral and contralateral hemispheres (1.46 ± 0.24 vs . 1.31 ± 0.22, respectively), with the contralateral side exhibiting values closer to those of healthy subjects (1.65 ± 0.20). Discussion The reproducibility of the DTI-ALPS index is significantly affected by user variability. Further research is needed to standardize ROI placement and improve image processing techniques to enhance the reliability of the ALPS index in clinical practice. Conclusion Being easily implemented, the DTI-ALPS index demonstrates some potential as a non-invasive biomarker for glymphatic system function, particularly in identifying pathological changes in glioma patients, considering evaluation in ipsi- and contralateral hemispheres.
Introduction Low-grade astrocytomas are slow-growing yet invasive brain tumors that may progress to high-grade forms if treatment fails. Post-surgical radiotherapy is essential but requires precise dose planning to maximize efficacy and minimize harm to healthy tissue. This study aims to predict optimal radiotherapy dosage and number of sessions for astrocytoma patients using MRI images and clinical data. Methods Data from 33 patients—including 2,745 MRI images (axial, sagittal, and coronal views, 512 × 512 pixels) and clinical/treatment information—were collected from the Mahdieh Radiation Oncology Department. Regression models were developed to estimate the number of radiotherapy sessions and dosage, while classification models assigned patients to one of four dose categories based on prior data. A hybrid feature extraction model combining a Vision Transformer (ViT) and Convolutional Neural Network (CNN) was used, followed by Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest algorithms. Results The CNN_VIT-b16 model delivered the best performance, predicting session numbers with a mean absolute error of 0.005 and R2 of 0.993, and dosage with a mean absolute error of 0.0034 and R2 of 0.998. In the classification task, it achieved an accuracy of 0.99 and an F1 score of 0.99 on the test data. Discussion The hybrid CNN-ViT model accurately predicted radiotherapy plans based on imaging and clinical features, supporting its role as a decision-support tool for personalized treatment. Nevertheless, further validation with larger, more diverse cohorts is necessary. Conclusion This study demonstrates that a diagnostic-aided model using MRI and clinical data can effectively personalize radiotherapy planning for astrocytoma, with promise for enhancing treatment precision and safety.
Introduction As digital imaging data are growing exponentially, compression of medical images is a critical issue for efficient storage and reliable transmission. As a result, researchers are continuously exploring methods for reducing the size of medical images further. Methods To further improve the compression methods, this paper proposes the maximum entropy-based threshold incorporated into the edge-based active contour method to automate the initialization of the curve for accurate extraction of the diagnostic or pathologically significant parts from unevenly illuminated Magnetic Resonance (MR) brain images. The images are then segmented into informative and background parts, which are further subjected to high-bit-rate and low-bit-rate compressions, respectively. This non-uniform compression results in an improvement in compression rate while preserving the quality of the diagnostic parts of the images. Results and Discussion The evaluation was performed on the dataset of MR brain images, and empirical analysis confirmed that the proposed method is able to outperform other existing methods in terms of segmentation and compression metrics. Conclusion The mathematical results of the proposed method indicate that the extracted area of the informative parts was similar to the object of interest in ground truth images. This accurate demarcation of the informative parts results in an improvement in compression rate without compromising the quality of the informative parts.
A thyroglossal duct cyst (TGDC) is a common congenital anomaly. However, the development of carcinoma within it is rare. Submental presentation and the concomitant TGDC carcinoma with thyroid gland carcinoma are indeed very rare. In this case report, a TGDC carcinoma with concomitant thyroid carcinoma in a fifty year-old Iraqi middle aged female presented with a submental mass. It was diagnosed initially as a sublingual ranula. Clinical examination showed a non-mobile tender hard mass at the submental region with no obvious thyroid gland enlargement. An imaging study showed a normal thyroid size and texture with a complex cystic-solid lesion involving the sublingual space. Fine needle aspiration cytology showed atypical follicular epithelial inflammatory cells within the thyroid nodule. The neck mass smear suggested papillary thyroid carcinoma, which was confirmed on surgery by Sistrunk procedure and postoperative histopathology. Subsequently, the patient was kept on radioactive iodine therapy. Papillary thyroid carcinoma arising in TGDC may present as a large complex midline mass at the upper neck or floor of the mouth and should be kept in mind even if there is no history of thyroglossal duct cyst or a history of thyroid nodule.
Bone age assessment represents an important step in the management of children with Isolated Growth Hormone Deficiency (IGHD). This study examined the usefulness of Ultrasound (US) in the assessment of bone age in a sample of Iraqi children with IGHD as compared to radiography as a reference. Additionally, it verified if patient gender and growth hormone therapy have an impact on US accuracy. An observational cross-sectional study recruited children with isolated growth hormone deficiency who were diagnosed and followed at the Alresafa Specialized Center for Endocrinology and Diabetes, Baghdad, Iraq, over 6 months. Children with IGHD from Iraqi nationality were recruited, while children from other nationalities or having multiple hormonal deficiencies, syndromic features, and parent refused participation were excluded. For each patient, a bone age assessment was conducted using two methods: US and TW2 hand-wrist radiographs at the same visit by the same radiologist. A total of 116 children were included. The chronological age of recruited children was 7 to 17 years, with a mean of 13.01 ± 2.78 years. There were 67 males (57.9%) with a male-to-female ratio of 1.37:1. The patient's gender did not affect the US accuracy; there was a non-significant difference in the bone age estimated by the US and conventional radiograph for both male and female patients, (p-value = 0.087, 0.308) respectively. Those who received growth hormone therapy and those who did not for both male and female patients (p-value = 0.071,0.243), respectively. There was a strong positive correlation between the means of bone age assessed by ultrasound (US) and conventional radiography for both males and females, with correlation coefficients of r = 0.788 and r = 0.703, respectively. Ultrasound may serve as a valid replacement for radiography in the assessment of bone age in children with short stature caused by a growth hormone deficiency, irrespective of the gender and treatment received. Thus, it may overcome radiography drawbacks for children who need sequential bone age assessment.
Invasive cribriform cancer of the breast (ICC) is a rare type of breast cancer characterized by its unique cribriform cell pattern. It is frequently seen in menopausal women, presenting with bloody nipple discharge or breast mass as an incidental finding. It is classified as a good prognosis tumor; however, it imposes diagnosis challenges due to its shared similarities with other breast cancers and coexistence with various histological subtypes. A 62-year-old lady presented with a 2-month history of breast discharge with a palpable mass. An imaging study revealed a BI-RADS 4 category (according to imaging reporting and data system), implying a definitive probability of malignant pathology. A core needle cytology suggested atypical hyperplasia or invasive malignancy; an excisional biopsy and immunohistochemistry study confirmed ICC diagnosis. The integration of histopathological examination with immunohistochemistry unveiled ICC diagnosis and excluded other differential diagnoses. Several challenges can be associated with ICC diagnosis, as it may present with subtle or atypical imaging criteria or even tend to regress, which is why radiologists should experience a high index of suspicion. This case reinforces the diagnostic complexities encountered during breast cancer management, emphasizing the need for a multidisciplinary approach and judicious use of diagnostic modalities to improve diagnosis precision and allow a tailored treatment plan for better patient prognosis and outcomes.
Estimation of accurate gestational age is critical in perinatal care. Traditional fetal biometrics measured via ultrasonography face limitations, especially in the third trimester. Fetal Kidney Length (FKL) has emerged as a promising biometric parameter associated with advanced Gestational Age (GA). This study aimed to examine the diagnostic accuracy of FKL in comparison with traditional parameters in late pregnancy to optimize patient management and outcome. A cross-sectional study enrolled 124 low-risk pregnant women with confirmed dating at 28-40 weeks of gestation. For every participant, two sets of data were collected: demographics (age, gravidity, date of last menstrual period) and fetal biometric parameters [Biparietal Diameter (BPD), Head Circumference(HC), Abdominal Circumference(AC), Femoral Length (FL), Amniotic Fluid Index(AFI), Estimated Fetal Weight (EFW), and Fetal Kidney Length (FKL)] including the length and width were calculated. Pearson's Correlation coefficients measured the strength of the association between GA and ultrasonic parameters. The mean KL for right and left (RKL, LKL) was 3.94±0.36 vs. 3.95±0.37 cm; p=0.84. FKL showed positive correlations with GA (r=0.54,0.52), p<0.001 for RKL, and LKL with determination coefficient r2= (0.29,0.27), respectively. GA was positively and strongly correlated to HC, AC, and FL with a correlation coefficient of 0.77, 0.76, and 0.73; p<0.001, respectively. The determination coefficient for the HC, AC, and FL were 0.29 and 0.27, respectively. FKL showed a moderate link to GA during the third trimester; it did not surpass traditional fetal biometric parameters. Still, FKL measurement had advantages: consistent values, independence of feto-maternal condition, and non-invasiveness and acceptability. These qualities recommended FKL for integration into routine prenatal care as a supplementary metric when other parameter calculations are challenging. Further research is warranted to examine FKL performance when combined with other biometrics and explore its diagnostic and prognostic applications.
Aim The aim of this study is to determine the most prevalent types of federated learning, discuss their uses in healthcare, highlight the most significant issues, and suggest methods for further research. Context When it comes to handling distributed data, federated learning is revolutionary, especially in sensitive sectors like healthcare. In order to improve the outcomes of the growing number of healthcare studies, there must be a method to safely and effectively analyze and use this enormous data. Objective The purpose of this research is to use a large corpus of 6,800 healthcare studies published between 2000 and 2024 and apply topic modeling using Latent Semantic Analysis (LSA). Methods The corpus was analyzed using LSA with the goal of identifying latent themes that capture the spirit of federated learning in the healthcare industry. In order to provide an organized overview of the subject matter, a five-topic solution was devised. To guarantee relevance and clarity, the topics' coherence was assessed. Results The term frequency and the inverse document frequency of high-loading terms provided five major topic solutions. The coherence score of the five-topic solution was achieved, i.e., 0.789, indicating a high level of relevance and integration among the identified topics. Different types of federated learning (FL), applications of FL, and the key challenges and the possible solution associated with FL have been analyzed. Conclusion This study highlights the significance of using FL to improve privacy-preserving data analysis in the healthcare field, which may lead to the development of creative solutions for complex problems.
Purpose:To investigate multivariate regional patterns for schizophrenia (SZ) classification, sex differences, and brain age by utilizing structural MRI, demographics, and explainable artificial intelligence (AI). Methods:Various AI models were employed, and the outperforming model was identified for SZ classification, sex differences, and brain age predictions. For the SZ and sex classification tasks, support vector classifier (SVC), k-nearest neighbor (KNN), and deep learning neural network (DL) models were compared. In the case of regression-based brain age prediction, Lasso regression (LR), Ridge regression (RR), support vector regression (SVR), and DL models were compared. For each regression or classification task, the optimal model was further integrated with the Shapley additive explanations (SHAP) and the significant multivariate brain regional patterns were identified. Results:Our results demonstrated that the DL model outperformed other models in SZ classification, sex differences, and brain age predictions. We then integrated outperforming DL model with SHAP, and this integrated DL-SHAP was used to identify the individualized multivariate regional patterns associated with each prediction. Using DL-SHAP approach, we found that individuals with SZ had anatomical changes particularly in left pallidum, left posterior insula, left hippocampus, and left putamen regions, and such changes associated with SZ were different between female and male patients. Finally, we further applied DL-SHAP method to brain age prediction and suggested important brain regions related to aging in health controls (HC) and SZ processes. Conclusion:This study systematically utilized predictive modeling and novel explainable AI approaches and identified the complex multivariate brain regions involved with SZ classification, sex differences, and brain aging and built a deeper understanding of neurobiological mechanisms involved in the disease, offering new insights to future SZ diagnosis and treatments and laying the foundation of the development of precision medicine.
Aim This study aims to enhance the precision of Alzheimer's disease (AD) detection by integrating Spatial Attention Mechanism into a Convolutional Neural Network (CNN) architecture. Background Alzheimer's disease is a progressive neurodegenerative disorder characterized by abnormal protein deposits in the brain, leading to nerve cell loss and posing a significant global health challenge. Early and accurate detection is crucial for disease management and treatment due to the lack of a cure and the disease's severe progression. Objective The objective of this research is to improve the accuracy of Alzheimer's disease classification using MRI data by implementing a Spatial Attention Mechanism in a CNN architecture. Methods The study utilized T1-weighted MRI data from the OASIS 1 and OASIS 2 datasets. The key innovation is the Spatial Attention layer incorporated within a CNN model, which computes the average of each channel in the input feature map. This layer guides subsequent layers to focus on critical brain regions, enhancing the model's accuracy in differentiating between Alzheimer's disease stages. Results The model achieved a validation accuracy of 99.69% with a sensitivity and specificity of 1.0000, demonstrating its reliability in distinguishing between different stages of Alzheimer's disease. The adaptability of the Spatial Attention layer allows the model to assign higher weights to crucial brain regions, improving its discriminative power. Conclusion The integration of the Spatial Attention Mechanism into the CNN architecture significantly contributes to the early detection of Alzheimer's disease, enabling timely interventions. This innovative approach has the potential to revolutionize Alzheimer's diagnosis by enhancing accuracy and offering a robust solution for classification.
Background Chest X-rays have long been used to diagnose pneumothorax. In trauma patients, chest ultrasonography combined with chest CT may be a safer, faster, and more accurate approach. This could lead to better and quicker management of traumatic pneumothorax, as well as enhanced patient safety and clinical results. Aim The purpose of this study was to assess the efficacy and utility of bedside US chest in identifying traumatic pneumothorax and also its capacity to estimate the extent of the lesion in comparison to the gold standard modality chest computed tomography. Methods This was an observational cross-sectional study of 160 patients with traumatic pneumothorax. This sample was collected from all chest trauma patients admitted to Al-Kindy Teaching Hospital in Baghdad-Iraq between November 2021 and September 2022. Such patients were to have a bedside chest US and chest CT scan performed by a skilled radiologist to detect lung point signs and lung sliding, which would be used to determine the patient's pneumothorax status. Results According to the study's findings, about 77.5% of the patients evaluated were men. Furthermore, 40.6% of patients experienced blunt trauma. Chest ultrasound revealed positive pneumothorax in 50 cases (31.2%), while positive pneumothorax was confirmed by computed tomography in 53 cases (33.1%) with no significance in the detection of pneumothorax between the two imaging modalities p-value(0.719). Comparably, there was no significant difference in estimating the size of a pneumothorax between the two modalities (p-value = 0.547). Chest ultrasound diagnostic accuracy showed a sensitivity of approximately 92.45%, specificity of 99.07%, and diagnostic accuracy of 96.88%. Conclusion Our findings indicated that chest ultrasound might be a valuable rapid diagnostic tool for traumatic pneumothorax in the emergency department in addition to diagnosis. It eliminates the need to transport patients for a CT chest scan.
Background Moyamoya disease (MMD) is an occlusive cerebrovascular condition characterized by progressive stenosis of the terminal portion of the internal carotid artery (ICA) and the development of an abnormal vascular network at the base of the brain. This disease predominantly affects individuals in East Asian countries, with an incidence rate ranging from 6.03 to 9.1 per 100,000 people. Case Presentation We report the case of a 41-year-old Hispanic woman who presented severe headaches, nausea, vomiting, and intermittent loss of alertness over a 15-day period. Upon admission, her vital signs were normal, and no focal neurological deficits were observed. Initial plain CT imaging revealed an interhemispheric subarachnoid hemorrhage with intraventricular involvement in the occipital recess and right atrium. Subsequent angiographic CT with 3D reconstructions exhibited the classic 'puff of smoke' appearance indicative of Moyamoya disease. Perfusion-weighted imaging (PWI) demonstrated normal relative cerebral blood flow, blood volume, and mean transit time in both hemispheres. Based on these imaging findings, the patient was diagnosed with MMD. She underwent an indirect revascularization procedure known as encephaloduroarteriosynangiosis, which involved suturing branches of the superficial temporal artery to the dura. Discussion This case report underscores an atypical presentation of MMD in a Hispanic patient diagnosed by a combination of digital subtraction angiography (DSA), 3D CT angiography, and brain perfusion MRI. The findings highlight the importance of recognizing and diagnosing this rare condition in populations outside of East Asia. Furthermore, this report includes a review of the updated literature on MMD, providing valuable information on its diagnosis and management. Conclusion The clinical presentation and imaging findings, in this case, underscore the need for advanced diagnostic techniques, such as perfusion-weighted imaging (PWI) and quantitative color-coded parametric DSA (QDSA), to improve diagnostic precision and treatment planning. The successful application of indirect revascularization through encephaloduroarteriosynangiosis demonstrates the efficacy of surgical interventions in the treatment of MMD. Addressing ethnic disparities in MMD is crucial to improving early diagnosis and patient outcomes. Future research should focus on refining treatment algorithms, investigating nonsurgical interventions, and examining cognitive and psychological outcomes to further improve patient care.
One of the most common mental diseases in childhood, attention-deficit/hyperactivity disorder (ADHD) often lasts into adulthood for many individuals. The neurodevelopmental condition known as ADHD impacts three areas of the brain: hyperactivity, impulsivity, and attention. The visual field is where attention is most affected by ADHD. Non-strabismic binocular vision disorder (NSBVD), which impairs eye coordination and makes it challenging to focus, has been linked to ADHD. Through a critical cognitive process called visual attention, humans are able to take in and organize information from their visual environment. This greatly affects how one observes, processes, and understands visual information in day-to-day living. Vision therapy is a non-invasive therapeutic approach that aims to improve visual talents and address visual attention deficits. This study aims to provide an overview of the research on the many approaches to treating ADHD, the relationship between NSBVD and ADHD, and whether vision therapy is a viable treatment option for ADHD. After a comprehensive search of many online resources, relevant studies were found. The review's findings provide insight into the range of ADHD patients' treatment choices. In order to improve treatment outcomes, non-pharmacological treatments can be employed either alone or in conjunction with medicine. Medicine by itself is insufficient and has several severe side effects when used continuously. The efficacy of vision therapy in improving visual attention and making recommendations for potential directions for further research in this field. Multiple studies are needed to identify the most effective treatment modalities for achieving positive outcomes for ADHD patients.
Background A complicated and clinically varied illness known as ADHD (“Attention-deficit/hyperactivity disorder”) leads to poor academic and professional outcomes, family stress, and financial difficulty. Worldwide, children and adults with attention-deficit/hyperactivity disorder are likely to suffer from all problems. ADHD are neurodevelopmental diseases that impact impulsivity, hyperactivity, and inattention. Basic academic skills like reading and arithmetic have been connected to visual search and sustained visual attention. Methodology The prevalence of attention-deficit/hyperactivity symptoms among students aged 17 to 23 in higher education institutions in Punjab, India, was investigated through a cross-sectional quantitative survey conducted from May to September 2023.. An online form was used to create the ADHD self-report scale (v1.1). This questionnaire was divided into 3-part inattention, hyperactivity, and impulsivity. The responses were categorized into five levels: Never, Rarely, Sometimes, Often, and Very Often. This Questionnaire was distributed to students from higher educational institutes, and data was collected. Result The total 360 student data were analyzed using SPSS 20. As the age increased, ADHD symptoms were reduced, inattention was most symptomatic in 18 and 19 years,` and hyperactivity and impulsivity symptoms had high scores in the 23 years age category. Out of the participants, 228 were female and 132 were male. Female students exhibited more symptoms of inattention (37.7%), while male students showed higher symptoms of hyperactivity (39.4%) and impulsivity (31.8%). Most students reported experiencing symptoms 'sometimes,' with responses indicating 'often' or 'very often' being rare across all three categories. This suggests that many respondents experience ADHD-related symptoms. Factors such as the number of siblings, family type, parents’ education level, and living arrangements did not impact the prevalence of ADHD symptoms. Conclusion The prevalence rate of ADHD symptoms among north Indian higher educational institutes was 23.3%. Among these ADHD-symptomatic students, inattention was 35%, hyperactivity 39.2%, and Impulsivity 26.9%, respectively.
